Method and system for detection of disease agents in blood

ABSTRACT

The invention principally relates to a method of detecting a disease agent in blood, comprising: (i) creating a sample infra-red spectrum representative of the blood, with one or more spectral components, each having a wavenumber and absorbance value; (ii) providing a reference database of spectral models, each model having one or more database spectral components of a wavenumber and an absorbance value, wherein the database spectral components identify disease agents; (iii) determining whether one or more database spectral components corresponds to one or more sample spectral components, and (iv) compiling a list of corresponding database components identified.

CROSS REFERENCE TO RELATED APPLICATIONS

The present application is a continuation of U.S. application Ser. No.15/521,206, filed on Oct. 23, 2015, now U.S. Pat. No. 10,145,839, whichis a U.S. National Phase of International Patent Application Ser. No.PCT/AU2015/000631, filed on Oct. 23, 2015, which claims priority toAustralian Patent Application Serial No. 2014904257, filed on Oct. 24,2014, the entire contents of which are hereby incorporated by referencein their entirety.

FIELD OF INVENTION

The present invention relates to the field of detection of disease,particularly blood borne disease agents. In a particularly preferredembodiment the present invention relates to detection and quantificationof infectious disease in blood.

In one form, the invention relates to a method of using Attenuated TotalReflection Infrared (ATR-IR) spectroscopy for detection, identificationand quantification of blood borne disease agents.

In another form, the invention relates to a method of multivariateanalysis of data obtained by ATR-IR from blood.

In one particular aspect the present invention is suitable for use fordiagnosis of blood borne infectious disease.

In one particular aspect the present invention is suitable for use fordiagnosis of malaria, human immune deficiency virus (HIV), or hepatitisB virus (HBV), or hepatitis C virus (HCV) infection from blood samples.

It will be convenient to hereinafter describe the invention in relationto malaria, however it should be appreciated that the present inventionis not limited to that use only and can be used for a wide range ofother blood borne infectious agents.

BACKGROUND ART

It is to be appreciated that any discussion of documents, devices, actsof knowledge in this specification is included to explain the context ofthe present invention. Further, the discussion throughout thisspecification comes about due to the realisation of the inventor and/orthe identification of certain related art problems by the inventor.Moreover, any discussion of material such as documents, devices, acts orknowledge in this specification is included to explain the context ofthe invention in terms of the inventor's knowledge and experience and,accordingly, any such discussion should not be taken as an admissionthat any of the material forms part of the prior art base or the commongeneral knowledge in the relevant art in Australia, or elsewhere, on orbefore the priority date of the disclosure and claims herein.

Attenuated Total Reflection Infrared (ATR-IR) Spectroscopy

Spectroscopy is the branch of science devoted to discovering thechemical composition of materials by examining the interaction ofelectromagnetic radiation with the material. Infrared (IR) spectroscopyrelates primarily to the absorption of energy by molecular vibrationshaving wavelengths in the infrared segment of the electromagneticspectrum, that is energy of wave number between 200 and 4000 cm⁻¹. Ramanspectroscopy relates to the inelastic scattering of monochromatic lightgiving wavelength shifts that depend on the molecular vibrations, havingtypically wave number shifts between 20 and 4000 cm⁻¹.

The structure of almost all biological molecules includes moieties thatabsorb energy in the IR segment of the electromagnetic spectrum. Thus,an IR spectrum of a clinical sample is representative of its mainbiological components and can be in the nature of a ‘metabolicfingerprint’.

ATR is a sampling technique that can be used in conjunction with IR. ATRspectroscopy offers the advantages of being potentially portable, it isinexpensive and thus has become a very powerful tool in the analysis ofbiological cells and tissues. ATR also allows samples to be examineddirectly in the solid or liquid state without further preparation, andcompared with transmission-IR, the path length into the sample isshorter, avoiding strong attenuation of the IR signal in highlyabsorbing media such as aqueous solutions.

In use, the sample is put in contact with the surface of a crystalhaving a higher refractive index than the sample. A beam of IR light ispassed through the ATR crystal in such a way that it reflects at leastonce off the internal surface in contact with the sample. Thisreflection forms an evanescent wave which extends into the sample. Thepenetration depth into the sample depends on the wavelength of light,the angle of incidence and the indices of refraction for the ATR crystaland the medium being probed. The number of reflections may be varied.The beam is then collected by a detector as it exits the crystal.

Viral Hepatitis

Viral hepatitis is caused by one or more of the six unrelatedhepatotropic viruses hepatitis A virus, hepatitis B virus, hepatitis Cvirus, hepatitis D virus, hepatitis E virus and hepatitis G virus.Millions of deaths occur annually around the world due to hepatitis.Diagnosis is made by assessing a patient's symptoms, physicalexamination and medical history in conjunction with blood tests, liverbiopsy and imaging. In many cases patients suffering hepatitis are notaware of symptoms, only becoming aware of the disease during routineblood tests.

But several liver diseases present with signs, symptoms or liverfunction test abnormalities similar to viral hepatitis. Accordingly, newdiagnostic techniques that can rapidly and inexpensively distinguishbetween these various diseases of the liver are being sought.Preferably, new diagnostic techniques can distinguish between thevarious hepatitis viruses.

Human Immunodeficiency Virus (HIV)

HIV is a lentivirus that causes acquired immunodeficiency syndrome(AIDS) comprising progressive failure of the immune system. Manypatients are unaware they have been infected by HIV and widespread,routine testing does not usually occur even amongst population sectorsat high risk of infection.

HIV testing is initially by enzyme-linked immunosorbent assay (ELISA)carried out in duplicate to detect antibodies-positive patients.Confirmatory testing is then carried out with a more specific test (egWestern blot or immunofluorescence assay). If Western blot alone isused, a second specimen is usually collected more than a month later andretested. Nucleic acid testing such as PCR testing can also be performedcan also help diagnosis.

Although these established testing regimes are very accurate, they areonerous in terms of time, labour and expense. Accordingly, newdiagnostic techniques that can rapidly and inexpensively detect HIVinfection are still being sought.

Malaria

Malaria is a mosquito borne disease caused by five parasitic protozoansof the genus Plasmodium, Plasmodium falciparum, Plasmodium vivax,Plasmodium ovale curtisi, Plasmodium ovale wallikeri, Plasmodiummalariae, Plasmodium knowlesi. There are up to 1.2 million fatalitiesper annum and accurate and early diagnosis followed by the immediatetreatment of the infection is essential to reduce mortality and preventoveruse of antimalarial drugs.

During the course of its life the malaria parasite transgresses throughseveral developmental stages including a sexual and an asexualreproductive pathway. New technologies to diagnose malaria must be costeffective and have high sensitivity and be able to detect circulatingstages of the malaria parasite namely the ring and gametocyte formsbecause these are the only stages present in peripheral bloodcirculation.

Optimally, the diagnosis of malaria in a patient is followed up by theappropriate antimalarial treatment which must be initiated immediately.Treatment should be guided by three main factors:

-   -   the identify of the infecting Plasmodium species;    -   the clinical status of the patient; and    -   the drug susceptibility of the infecting parasites as determined        by the geographic area where the infection was acquired and the        previous use of antimalarial medicines

Determination of the infecting Plasmodium species for treatment purposesis important for three main reasons. Firstly, Plasmodium falciparum andPlasmodium knowlesi infections can cause rapidly progressive severeillness or death while the other species such as Plasmodium vivax,Plasmodium ovale, or Plasmodium malariae, are less likely to causesevere manifestations. Secondly, Plasmodium vivax and Plasmodium ovaleinfections also require treatment for the hypnozoite forms that remaindormant in the liver and can cause a relapsing infection. Finally,Plasmodium falciparum and Plasmodium vivax species have different drugresistance patterns in differing geographic regions. For Plasmodiumfalciparum and Plasmodium knowlesi infections, the urgent initiation ofappropriate therapy is especially critical.

Efforts have also been made to investigate the potential of synchrotronFourier Transform Infrared (FTIR) in combination with PrincipalComponent Analysis (PCA) to differentiate between intraerythrocyticstages or the parasite life cycle based on the molecular signatures ofHz and specific lipids (Webster et al. Disciminating theIntraerythrocytic Lifecycle Stages of the Malaria Parasite UsingSynchrotron FT-IR Microspectroscopy and an Artificial Neural Network,Analytical Chemistry 2009, 81, 2516-2524). Webster et al found that asthe parasite matures from its early ring stage to the trophozoite andfinally to the schizont stage there is an increase in absorbance andshifting of specific lipid bands.

This work demonstrated the potential of using FTIR spectroscopy as adiagnostic tool for malaria but clearly a synchrotron-based method isnot suitable for field use or for routine laboratory use.

FTIR spectroscopic diagnosis relies on precise acquisition of spectra,spectral pre-processing and chemometric tools such as Artificial NeuralNetwork analysis (Lasch et al., J. Chemometr. 20, 209-220 (2006)) orUnsupervised Hierarchical Cluster Analysis (Bambery et al., Biochim.Acta. 1758, 900-907 (2006); Wood et al., Gynecol. Oncol. 93, 59-68(2004)).

There is enormous spectral variation between biological samples causedby a number of factors, including spectral scatter caused by preparativetechniques and scattering artifacts which have hindered progress of FTIRas a clinical diagnostic tool. Furthermore, the some biological moietieshave infra-red molar absorptivity characteristics that do not complywith Beer-Lambert law.

FTIR spectroscopic methods have also been used for the detection ofcancerous and precancerous cells and tissues (Whelan et al., J.Biophotonics 6, No. 10, 775-784 (2013)/DOI 10.1002/jbio.201200112). Inorder to overcome non-Beer-Lambert infra-red absorption behaviour,simple statistical models were developed to predict the concentration ofDNA in cells. However it is acknowledged in this study that the simplemodels developed from the study would not be adequate for complicatedcells or complex mixtures of biological compounds.

In another study (Sitole et al, OMICS A J. Integrative Biol. 18(8)513-523 (2014), mid-ATR-FTIR spectroscopic profiling of blood sera hasbeen explored as a diagnostic for HIV/AIDS. While the system isjudicative of the promise for diagnosis it is clear that problems arisewith the data used due to modelling based on artifactual differences. Inparticular separation is observed in loadings plots due to differencesin bound water and artefacts resulting due to a lack of ATR correctionon the spectra.

Accordingly, there is a need for new methods to enable wider use of FTIRas a diagnostic.

SUMMARY OF INVENTION

An object of the present invention is to provide a method suitable fordetecting identifying and quantifying blood borne infectious diseaseagents.

An object of the present invention is to provide a method suitable forfield use or laboratory use for detecting and quantifying bipod borneinfectious disease.

An object of the present invention is to provide a method suitable fordetecting and quantifying blood borne infectious disease agents in asample of whole blood, or plasma, or blood cells, or dried whole blood.

A further object of the present invention is to alleviate at least onedisadvantage associated with the related art.

It is an object of the embodiments described herein to overcome oralleviate at least one of the above noted drawbacks of related artsystems or to at least provide a useful alternative to related artsystems.

In a first aspect of embodiments described herein there is provided amethod of detecting a disease agent in a blood sample, the methodcomprising the steps of:

-   -   (i) creating an infra-red sample spectrum representative of the        blood sample, the sample spectrum having one or more spectral        components, each component having a wavenumber and absorbance        value,    -   (ii) providing a reference database of spectral models, each        model having one or more database spectral components of a        wavenumber and an absorbance value, wherein the database        spectral components identify disease agents,    -   (iii) determining whether the reference database has one or more        database spectral components corresponding to one or more sample        spectral components, and    -   (iv) compiling a list of corresponding database components        identified.

Preferably, step (ii) of the method further includes selecting one ormore spectral windows in which to undertake step (iii).

Typically the IR spectrum is created by delivering an evanescent IR beamthrough an ATR substrate in contact with a patient blood sample. Thiscan be obtained putting or drying the sample on the ATR or from a thickblood film on a slide, such as a glass or plastic slide. Otheralternative is to obtain the, the IR spectrum is created using focalplane array, that is, focal plane spectroscopic imaging of thin bloodsmears on glass.

Most laboratories in the developing world currently use thick bloodfilms on slides for routine microscopy. It is therefore particularlyadvantageous that the method of the present invention can be used toanalyse thick films on slides without changing the current laboratorymethodology. Furthermore, the method of the present invention can beused to detect disease agents in archived thick film blood samples.

The present invention also advantageously provides the option of using aminimal volume of blood, such as a single drop of less than 50 μl, morepreferably between 5 and 25 μl. Most infectious disease diagnostictechniques require far larger volumes of blood.

In one particular aspect the present invention is suitable for use fordiagnosis of human immune deficiency virus (HIV), or hepatitis B virus(HBV), or hepatitis C virus (HCV) infection from blood samples or otherblood borne viral diseases including viral haemorrhagic viruses whichincludes and is not limited to viruses of several viral familiesincluding Arenaviridae (Lassa fever, Junin and Machupo), Bunyaviridae(Crimean-Congo haemorrhagic fever, Rift Valley Fever, Hantaanhaemorrhagic fevers). Filoviridae (Ebola and Marburg) and Flaviviridae(yellow fever, dengue, Omsk haemorrhagic fever, Kyasanur forest disease,West Nile virus), viruses that are transmitted by arthropods or vectorssuch as those of the Alphaviridae. Other blood borne infectious diseasesof man may be transmitted by parasites which includes malaria (includingdifferentiation between the various phases of malaria and variousspecies). African trypanosomiasis, babesiosis, Chagas disease,leishmaniasis, and toxoplasmosis. Parasitic blood borne agents includesBabesia B. divergens, B. bigemina, B. equi, B. microfti, B. duncani,Leishmania Toxoplasma gondii, Plasmodium falciparum, Plasmodium vivax,Plasmodium ovale curtisi, Plasmodium ovale wallikeri, Plasmodiummalariae, Plasmodium knowlesi, Trypanosoma brucei and Trypanosoma cruzi.

Blood Sample

The patient blood sample may be in any convenient form, such as wholeblood, plasma, blood cells, dried whole blood or combinations thereof.Preferably the blood sample is whole blood and can be collected by asimple finger prick and delivered directly to an IR spectrometer, oronto a glass or plastic slide.

But whole blood is not always optimal for diagnosis of a particulardisease. The form used will depend on a wide range of factors includingconvenience and the availability of apparatus for processing wholeblood. In particular it should be noted that variation in IR spectrarelating to a specific disease can be due to other factors notassociated or relevant to the disease. Those factors can mask theabsorbances or patterns of interest and make it difficult to compile alist of sample components corresponding to a spectra model in thedatabase.

Accordingly, it may be necessary to carry out a simple whole bloodpre-processing step to eliminate uncorrected components such as water(‘dry’ sample), cells (serum sample), proteins and polar compounds(organic extracted sample) or isolate the compounds of interest such aswhite blood cells or lipids. Carrying out a pre-processing step can makepossible the diagnosis of a disease otherwise undetectable by directmeasurement of a whole blood sample.

Preferably the blood sample is ‘wet’, that is in a liquid form and notdried. The “wet” blood sample in one form may contain a solvent that isnaturally occurring, such as water—or deliberately added, such asmethanol.

Wet samples include, for example, whole blood collected by a fingerprick from a patient. In the field the whole blood sample may be lysedand solvent added, usually water. Alternatively, the whole blood may befractioned to isolate plasma. Typically, fractionation would be carriedout in a laboratory where suitable equipment is readily available ratherthat in the field. For example, the whole blood sample may be extractedwith a solvent such as chloroform to extract lipid bilayers foranalysis.

As a less preferred alternatively to wet samples, the patient bloodsample may be ‘dry’ that is, having naturally occurring or added solventdriven off by drying in air, under a heat lamp or by another dryingprocess.

Spectral Components Identifying Disease Agents

Disease agents present in the blood, such as infectious agents includebiological moieties that absorb energy to create a signal in the mid-IRrange. The energy may be absorbed directly by a disease agent—such as avirus present in the blood. Alternatively the energy may be absorbed byother biological entities which are indicative of the presence of thedisease agent. Metabolic changes caused by some disease agents can leadto elevated blood levels of species such as glucose or urea, thusindirectly indicating the presence of a disease agent.

For example, if the disease agent is malaria, the blood sample willexhibit IR absorption bands associated with the molecular phenotype ofmalaria which is all the molecular and chemical components associatedwith the infectious agent generated during the reproduction cycles ofthe malaria parasite. Other indirect absorption may occur due to thedisease agent modifying the metabolic state of the patient includingmodifying the host cells and its response to an infectious agent.Importantly the present invention is potentially capable ofdistinguishing between the five parasitic protozoans of the genusPlasmodium. For example, tests using Plasmodium falciparum Plasmodiumvivax and Plasmodium malariae have confirmed that spectral differencescan be detected between these three species and the method of thepresent invention can distinguish between them.

Each virus and/or infectious agents have unique characteristics such asfor HIV these particles contain single-strand RNA which tightly bound tonucleocapsid proteins, late assembly protein, and enzymes essential tothe development of the virion, such as reverse transcriptase andintegrase. There will be also unique replicative forms within cells forinfectious agents capable of infecting cellular components within bloodcells. For example, HIV infects vital cells in the human immune systemsuch as helper T cells (specifically CD4+ T cells), macrophages, anddendritic cells.

As such there is the potential to detect IR absorption bands thatcorrespond to the molecular phenotype for each infectious agent andincludes viral replicative intermediates, “free” viral particles andalso the changes to the infected cells. Other blood borne infectionsagents may not be actively replicating within whole blood but virusparticles or other viral components may be found within the blood suchas for hepatitis B virus in which three forms are found. The mostabundant are small, spherical, noninfectious particles, containingHBsAg, that measure 17 to 25 nm in diameter. Concentrations of 1013particles per ml or higher have been detected in some sera. Tubular,filamentous forms of various lengths, but with a diameter comparable tothat of the small particles, are also observed. They also contain HBsAgpolypeptides. The third morphological form, the 42 nm hepatitis Bvirion.

The following table (Table 1) and FIG. 3 show typical IR bands ofbiological compounds and their respective assignments. The presence ofan infectious disease in the blood can be directly or indirectly relatedto the IR spectra through those bands.

TABLE 1 Wavenumber Referenced (cm⁻¹) Assignment Analytes to FIG. 3 930-1300 ν_(s) (C—O) Saccharides 140 1000-1150 ν_(s) (P—O)Phospholipids, 140 DNA 1150-1300 ν_(as) (P—O) Phospholipids, 140 DNA1200-1400 Amide III Proteins 103, 150 1430-1480 δ(CH₃), δ(CH₂) Lipids130 1480-1600 Amide II Proteins 102, 150 1600-1720 Amide I Proteins 101,150 1700-1760 ν_(s) (C═O) Lipids 120 2840-2860 ν_(s) (CH₂) Lipids 1102860-2870 ν_(s) (CH₃) Lipids 110 2870-2950 ν_(as) (CH₂) Lipids 1102950-2990 ν_(as) (CH₃) Lipids 110 3000-3020 ν (CH) Lipids 110 4000-4550ν (CH) combinations Lipids 110 4550-5000 ν (NH) ν (OH) Proteins,combinations saccharides 5600-6050 1° overtone ν (CH) Lipids 6700-71501° overtone ν (NH) Proteins, ν (OH) saccharides 8000-9100 2° overtone ν(CH) Lipids  9100-10500 2° overtone ν (NH) Proteins, ν (OH) saccharides11520-11760 3° overtone ν (CH) Lipids 11765-12900 3° overtone ν (NH)Proteins, saccharides

For example, FIG. 19 illustrates an IR spectrum of a whole blood samplewith a high viral load of HIV after subtraction of control spectra.Differences have then been enhanced and the spectrum can be divided upas follows:

-   -   (A) The absorption bands at approx. 1010 to 1050 cm⁻¹ and        approx. 1140 to 1190 cm⁻¹ are enhanced in the case of a virus.        Since they are assigned to RNA and DNA they are potentially        attributable to the virus itself (direct relationship).    -   (B) The absorption band at approx. 1380 to 1410 cm⁻¹ is also        enhanced in the case of a virus. It is difficult to establish        their origin but it is not a typical nucleic absorption band,        and is due to the influence of the virus on other metabolites        (indirect relationship).    -   (C) The absorption bands in the regions of approx. 1060 to 1150        cm⁻¹, approx. 1195 to 1340 cm⁻¹ and approx. 1410 to 1690 cm⁻¹,        often show different patterns for different samples. Therefore        they are not connected with the presence of a virus. They may be        associated with other biological factors not associated with        disease states.

Thus, HIV can be identified according to bands (A) and (B). Howeverspectral features of (C) not associated with the disease could dominategiven the enormous natural variation in blood samples. Accordingly, itcan be difficult to obtain sufficient sensitivity and specificity if asingle wavenumber were relied upon for detection and it is preferable tolook for a pattern in the spectrum using machine learning algorithms.

Spectral Models

The aforementioned pattern is typically found through a model expressedin terms of the range of operations expected to be performed by theclassification function (that is the possible g(x) of f=g(x)). Analgorithm is a mathematical procedure (normally iterative) employed forestablishing g(x). In the present invention, the model consists of analgorithm (Y_(Presence of the disease)=f(X_(Spectrum)) establishing themathematical relationship between the spectrum (X) of a blood sample andsome attribute of the blood sample (Y), that is, spectral componentsidentifying the disease agents.

FIG. 20 is a flow chart illustrating how spectra with, and without thedisease are input to the algorithm, to learn the characteristic spectralcomponents of the spectra for each class (positive for presence of thedisease, negative or unknown) to thus form a ‘calibration matrix’. Fromthis is provided a set of mathematical operations that can be applied tothe spectrum (a vector of absorbance values) for a new blood sample togenerate a value that determines if the spectrum can be classified aspositive, negative or unknown.

A model may be specific for one specific disease agent (eg separatemodels for HIV and Hepatitis B virus) or combinations of disease agents.Models of combinations are particularly useful for two or more diseaseagents that are frequently identified together (eg HIV+Hepatitis Bvirus).

Algorithms for Classifications

Algorithms suitable for use in the present invention include, but arenot limited to the following:

-   -   Linear modelling (LDA, PLSDA, SIMCA);    -   Neural Network analysis (NNA);    -   Random Forest (RF);    -   Supported Vector Machine (SVM).

Typically, the performance of all the algorithms for each dataset isindependently investigate. The selection of the best modelling system isbased normally in the prediction results obtained. It is also possibleto use aggregation models (that is, for each sample each modellingsystem gives a vote for a class and the final prediction is obtained bycomputing the mode of those votes).

It is noted there are no “best models” established for the diagnosis ofa particular disease agent. Depending on different factors inherent inthe disease agent, the number of samples and the variability of thosesamples, some algorithms show more classification performance thanothers. Accordingly, for each application a prior deep study of thedifferent modelling possibilities should be performed. Optimally, allthe variables involved in the modelling should be established.

Some pre-processing of the spectra may be carried out to prepare themfor the modelling in order to improve the model performance.

As described below, selection of a set of wavenumbers can enable abetter classification performance.

Other variables that may be optimised to create a useful model includethe number of trees in the RF and latent variables in the PLSDA.

In a second aspect of embodiments described herein there is provided amethod of creating a spectral model for use in a reference database ofspectral models for detecting a disease agent in a blood sample, themethod comprising the steps of:

-   -   (i) collecting calibration infra-red spectra representation of        blood samples carrying the disease agent and blood samples        absent the disease agent, the calibration spectra having one or        more spectral components, each component having a wavenumber and        absorbance value; and    -   (ii) creating an algorithm        Y_(Presence of the disease)=f(X_(Spectrum)) establishing the        relationship between the spectrum (X) of said blood samples and        the spectral components of the blood samples (Y) that identify        the disease agent.        Creation of a Model

Step typically used in the creation of a suitable model include thefollowing and are illustrated in FIG. 23:

Obtaining a Sample Set:

A set of blood samples and their reference data (that is, whether theyare positive or negative for a given disease agent) are collected. IRspectra of the blood samples are recorded and used to create a matrix X(n=number of samples x v=number of wavenumbers) and a vector ofreference data Y (nx1). The blood samples are classified in 3 differentsubsets: calibration, validation and test set.

Preprocessing of the Spectra:

Mathematical operations may be performed on the aforementioned spectraprior to the modelling with the aim of removing extraneous externalsources of variation and enhancing the differences of the spectraconnected with the bands of interest. These mathematical operationsinclude, for example:

-   -   correction of baseline shifts and path length changes,    -   correction of spectral artefacts related to the experimental        procedure (atmospheric contributions, scattering or ATR        correction), and    -   enhancement of the difference of the bands, especially in the        maximum position of the bands, among spectra by the use of        derivatives or mean centering.

Variable Selection Procedure:

Non-informative parts of the spectra may be removed in order to improvethe accuracy of the classification. In particular, it is preferable touse a limited number of wavenumbers (selected spectral windows) that arerelevant to a particular disease. For example for some disease agentsthe informative part of the spectrum may be the C-H(stretch)characterising lipids at 3100 to 2700 cm⁻¹. For proteins, nucleic acidsand carbohydrates the informative part of the spectrum may be from 1800to 900 cm⁻¹. The informative window of the spectrum for moietiesexhibiting amide 1, 2 or 3 stretching modes are more likely to be at1800 to 1200 cm⁻¹.

It is possible to use multiple spectral windows and process themsequentially or simultaneously.

The use of two or more selected spectral windows typically improves theaccuracy of results obtained using the model. For example FIGS. 24 to 27illustrate PLS-DA performed (as described below) on a sample of redblood cells (fixed with methanol) using one spectral window (FIGS. 24and 25) and two spectral windows (FIGS. 26 and 27), using samplespositive for malaria, and control samples.

Specifically, FIGS. 24 and 25 relate to use of the spectral windowcorresponding to lipids associated with malaria and correspond toConfusion Table (CV) (Table 2):

TABLE 2 Control Actual Malaria Predicted as Control 10 11 Predicted asMalaria 3 84

FIGS. 26 and 27 relate to use of the spectral window corresponding tolipids associated with malaria in addition to the spectral windowcorresponding to the C-O and P-O region for malaria and correspond toConfusion Table (CV) (Table 3):

TABLE 3 Control Actual Malaria Predicted as Control 13 1 Predicted asMalaria 0 92

Clearly the use of one window resulted in some false positives and somefalse negatives, but the performance of the model is improved when twospectral windows are used.

Modelling:

After the aforementioned pre-processing and variable selection steps,only calibration data is employed for building the model. The algorithmis applied and a function y=f_(selected(x)) is established. This processcould also serve to set-up some internal parameters of the model.

Optimisation of Parameters:

In this step the function is applied to the spectra of a validation setof samples. The Y_(val) returned by the function is compared with theone of the reference data, thus identifying any classification error. Atthis point the parameters (pre-processing, variable selection, type ofmodel, algorithm, and other variables) can be changed and the new f(x)can be obtained, with a corresponding associated error. One of thefunctions (and associated parameters) having a classificationperformance which fulfils the requirements of the application can beselected y=f_(selected(x)). Thus, the validation set is used only totune all the parameters involved in the modelling, created using thetraining set.

It will be readily apparent to the person skilled in the art that theprocess may be iterative, but selection of the parameters can also becarried out by various other approaches, or combinations of approachesincluding:

-   -   using the knowledge the character of the analytical problem; for        example, if the model is not linear, PLSDA is not used or CO₂        region is not used for classification, and    -   using Iterative procedures (for example a genetic algorithm in        the variable selection).

Optimisation:

The optimisation step comprises: calculation of the classification errorusing all the possible values of the variables for example, andselection of the latent variables in the PLSDA. This procedure can alsobe performed using the CV of the training set if the number of samplesis limited and a validation set is not available.

Testing the Model:

The actual classification capability of the function selected in theModelling step is then evaluated using and independent test of samples(that is, the X_(test) spectra of the test samples is introduced as aninput in the function and the output Ŷ_(test) is compared with thereference data Y_(test). The error value is the expected error of theclassification. If assumable, the model is ready to be used in a newpatient blood sample of unknown disease status.

Diagnosis of a New Sample:

Once the appropriate y=f_(selection(x)) is established with known errorlimitations, it can be applied to a spectrum of a new sample from apatient to determine a response (positive and negative).

It will be readily appreciated that the method used also requiresmonitoring to ensure ongoing accuracy. This can be carried out usingcontrol samples which can be predictive of future changes in the model.

FIG. 21 illustrates a preferred embodiment of model creation, andapplication to a blood sample having an unknown disease status. Themodel may be linear or non-linear. But in this preferred embodiment, alinear model is created based on discriminant analysis by partial leastsquares algorithm (PLS DA), to provide a vector of weights of eachwavenumber (i), the ‘regression vector’:W=(w ₁ ,w ₂ ,w ₃ . . . w _(i))

As described previously, it is preferable to use a limited number ofwavenumbers (selected band widths) that are relevant to a particulardisease. There are various ways to select those wavenumbers, from thestraightforward selection of the regions of interest to complexiterative selections such as genetic algorithm.

Considering the spectra as a vector of absorbance values:X=(x ₁ ,x ₂ ,x ₃ . . . x _(i))

The final outcome is calculated by multiplying the regression vectorW(i) by the absorbance values of the spectrum at each absorbance X(i):Y=w ₁ x ₁ +w ₂ x ₂ +w ₃ x ₃ . . . w _(i) x ₁)

Y values close to +1 are assigned to one class (eg positive for thedisease agent) and Y values close to 0 are assigned to the other class(eg negative for the disease agent). It will be appreciated that thecut-off values relating to assignment to one class or the other arearbitrary and is one of the variables that can be optimised, or altered.This might be appropriate for example, if it is preferred to have morefalse positives than false negatives.

Accordingly, the method of the present invention may include a furtherstep of allocating or flagging a blood sample as being positive for adisease, negative for a disease, or of unknown disease state. Thus thefurther step may comprise:

-   -   (iv) determining the number of sample components in each        respective spectral model compiled and ranking said compiled        spectral model.

Alternatively, the further step may comprise:

-   -   (iv) determining the number of sample components in each        respective spectral model compiled and classifying said compiled        spectral model based on predetermined classification criteria.

FIG. 22 includes a flow chart depicting the method of the presentinvention for creating a model to classify samples as positive ornegative for hepatitis viruses (Hep) that includes hepatitis B virus(HepB) and Hepatitis C virus (HepC). The model according to the presentinvention can be used to identify a specific hepatitis virus (hepatitisA, hepatitis B, hepatitis C, hepatitis D, hepatitis E, hepatitis G) orcombinations of hepatitis viruses.

Specifically, FIG. 22 reflects calibration using 10 blood samples (serumsamples) loaded with HepB, 10 serum samples loaded with HepC and 11serum samples loaded with HIV. The preprocessing steps includecalculation of a first derivative and mean centering. The variableselection step consist of choosing an appropriate fingerprint area(900˜1750 cm⁻¹).

A PLS model is then combined with a NINPALS algorithm, an interativealgorithm used in PLS for obtaining the weights of regression vectors.Another parameter used is the number of LV(4) Y=fselected(x): Regressionvector.

The validation step is carried out using a limited set of samples(rather than the validation set available) with cross validation. Asindicated in FIG. 22 two samples (sample nos 22 and 33) are used to testthe model. (NB: This is for illustration only. Typically far moresamples would be used to test the model.) Once the model is suitable forclassifying 100% of samples it is ready to be used to classify thestatus of an unknown blood sample from a patient.

FIG. 20 illustrates how in a first step are created calibration spectraare collected using blood samples known to be loaded or free of adisease agent. A reference database of spectral models is defined by aregression vector. An IR spectrum representative of a new blood sample(presence of disease agent unknown) is created and applying saidspectrum to a reference database of spectral models to identify one ormore spectral components of wavenumber and absorbance of the bloodsample. The spectral components identify disease agents in this case aseither hepatitis virus (HBV or HCV) or HIV. A list of sample componentsis identified corresponding to a respective spectral model of thedatabase. Finally the new blood sample is classified as positive ornegative with respect to hepatitis virus (HBV or HCV) or HIV.

System & Device

The generation of spectral models, preparation of calibration spectrafrom known blood samples knows to positive or negative for a specificdisease agent can be carried out in a laboratory. Once these aregenerated the algorithm and model function can be incorporated into asoftware application (app) and forwarded wirelessly to a user.

Thus in another embodiment the present invention provides a computerreadable storage medium for storing in non-transient form an applicationfor executing a method of detecting a disease agent in a blood sample,comprising the steps of:

-   -   (i) recording an IR spectrum representative of the blood sample;    -   (ii) comparing said spectrum to a reference database of spectral        models to identify one or more spectral components of wavenumber        and absorbance of the blood sample, wherein the spectral        components identify disease agents; and    -   (iii) compiling a list of sample components identified        corresponding to a respective spectral model of the database;        wherein steps (i) to (iii) are automated.

It will be clear to the person skilled in the art that in addition tothe application being stored on a computer readable storage medium, itmay be stored in the cloud or other computing equivalent. There is thusprovided an application adapted to enable the detection of a diseaseagent in a blood sample, said application comprising a predeterminedinstruction set adapted to enable a method comprising the steps of:

-   -   (i) creating a sample infra-red spectrum representative of the        blood sample, the sample spectrum having one or more spectral        components, each component having a wavenumber and absorbance        value,    -   (ii) providing a reference database of spectral models, each        model having one or more database spectral components of a        wavenumber and an absorbance value, wherein the database        spectral components identify disease agents,    -   (iii) determining whether the reference database has one or more        database spectral components corresponding to one or more sample        spectral components, and    -   (iv) compiling a list of corresponding database component        identified.

Thus, to operate the method of the present invention it is onlynecessary to have a means for generating an IR spectrum of a patientblood sample (such as a standard FT-IR spectrometer and a diamondcrystal ATR accessory) and the model function to derive a diagnosis.This means that the method of the present invention can adapted to arelatively small size that is suitable for field use, even in remotelocations.

Thus in a further embodiment, the present invention provides a systemfor detecting a disease agent in a blood sample, the system comprising aspectrometer for capture of an IR spectrum and a computer,

wherein

-   -   (i) the spectrometer creates an IR spectrum representative of        the blood sample,    -   (ii) the computer applies said spectrum to a reference database        of spectral models to identify one or more spectral components        of wavenumber and absorbance of the blood sample, wherein the        spectral components identify disease agents, and    -   (iii) the computer compiles a list of sample components        identified corresponding to a respective spectral model of the        database.

Other aspects and preferred forms are disclosed in the specificationand/or defined in the appended claims, forming a part of the descriptionof the invention. In essence, embodiments of the present invention stemfrom the realization that patterns of absorbance within IR spectra ofblood samples can be reviewed using machine learning algorithms or thelike and used to identify the presence or absence of disease states.

Advantages provided by the present invention comprise the following:

-   -   rapid detection of infection facilitating rapid treatment;    -   rapid identification of infectious agent present in vivo;    -   a straightforward, low cost diagnostic method;    -   a method that can use existing equipment;    -   can be used with existing blood sample collection techniques        such as the use of thick film slide samples;    -   can be used for the detection of a wide range if disease agents        including HIV, hepatitis A virus, hepatitis B virus, hepatitis C        virus, hepatitis D virus and hepatitis E virus, hepatitis G        virus or other blood borne viral diseases including viral        haemorrhagic viruses which includes and is not limited to        viruses of several viral families including Arenaviridae (Lassa        fever, Junin and Machupo), Bunyaviridae (Crimean-Congo        haemorrhagic fever, Rift Valley Fever, Hantaan haemorrhagic        fevers), Filoviridae (Ebola and Marburg) and Flaviviridae        (yellow fever, dengue, Omsk haemorrhagic fever, Kyasanur forest        disease, West Nile virus), viruses that are transmitted by        arthropods or vectors such as those of the Alphaviridae.        Parasitic blood borne agents includes Babesia B. divergens, B.        bigemina, B. equi, B. microfti, B. duncani, Leishmania        Toxoplasma gondii, Plasmodium falciparum Plasmodium vivax,        Plasmodium ovale curtisi, Plasmodium ovale wallikeri, Plasmodium        malariae, Plasmodium knowlesi, Trypanosoma brucei and        Trypanosoma cruzi;    -   can distinguish between similar or closely related agents        present in a blood sample;    -   can utilise a minimal volume (droplet) or blood.

Further scope of applicability of embodiments of the present inventionwill become apparent from the detailed description given hereinafter.However, it should be understood that the detailed description andspecific examples, while indicating preferred embodiments of theinvention, are given by way of illustration only, since various changesand modifications within the spirit and scope of the disclosure hereinwill become apparent to those skilled in the art from this detaileddescription.

BRIEF DESCRIPTION OF THE DRAWINGS

Further disclosure, objects, advantages and aspects of preferred andother embodiments of the present application may be better understood bythose skilled in the relevant art by reference to the followingdescription of embodiment taken in conjunction with the accompanyingdrawings, which are given by way of illustration only, and thus are notlimitative of the disclosure herein.

The figures relate to the following:

FIG. 1 displays are spectra obtained from different sets of samples;whole blood (1), lysed whole blood (3), plasma (5), RBC (7), RBC inmethanol (9), and slurry of coagulated blood (11).

FIG. 2 is an ATR-FTIR spectrum for organic extracts of RBC (20), wholeblood (21) and plasma (22) blood lipid extracts;

FIG. 3 illustrates typical IR bands associated with biological compoundsand their assignments to various moieties in the compounds—vCH₃, vCH₂,vCH, (23); vCO (24); v(P-O), v(C═O) (25); δCH₂ and δCH₃ (26); aminoacids (27) and amide I, amide II and amide III (28);

FIG. 4 is an ATR-FTIR spectrum of sera having different loads ofHepatitis B virus (30), Hepatitis C virus (31) and HIV (32), FIG. 4Ashowing the full spectrum from 3000 to 1000 cm⁻¹, FIG. 4B showing anexpansion of the region from 1750 to 900 cm⁻¹; and FIG. 4C showing anfurther expansion of the region from 1250 to 950 cm⁻¹ which isassociated with nucleic acids. A visual inspection shows some bands inthe spectra of samples infected by the HIV around 970 and 1160 cm⁻¹.Those bands (black arrows), do not appear in the spectra obtained fromHepatitis infected patients, and can be related to the presence of RNAand DNA.

FIG. 5 is an ATR-FTIR spectrum of whole blood, including a controlsample (35), and samples having different loads of HIV (36) andHepatitis C virus (37). FIG. 5A shows the full spectrum from 3000 to1000 cm⁻¹, FIG. 5B shows an expansion of the region from 1750 to 900cm⁻¹; and FIG. 5C shows a further expansion of the region from 1250 to950 cm⁻¹ which is associated with nucleic acids. In this case the bandsat the HIV spectra observed around 1140 and 1110 cm⁻¹ are even moreobvious.

FIG. 6 is an ATR-FTIR spectrum of samples having HIV loading including acontrol sample (40), whole blood (41) and serum (42). FIG. 6A shows thefull spectrum from 3000 to 1000 cm⁻¹, FIG. 6B shows an expansion of theregion from 1750 to 900 cm⁻¹; and FIG. 6C shows a further expansion ofthe region from 1250 to 950 cm⁻¹ which is associated with nucleic acids.Again, the bands specifically assigned to nucleic are shown, being muchmore intense in the case of the WB. IT may be caused by the presence oflymphocytes in the WB. Those bands are not found in the control understudy.

FIG. 7 is an ATR-FTIR spectrum of a control and serum sample loaded withhepatitis B virus. FIG. 7A shows the full spectrum from 3000 to 1000cm⁻¹, FIG. 7B shows an expansion of the region from 1750 to 900 cm⁻¹,and FIG. 7C shows a further expansion of the region from 1250 to 950cm⁻¹ which is associated with nucleic acids. Naked eye cannot founddifferences between the spectra of the control (45) and the pathologicalsamples of hepatitis B virus (46).

FIG. 8 is an ATR-FTIR spectrum of a control, whole blood and serumsamples loaded with hepatitis C virus. FIG. 8A shows the full spectrumfrom 3000 to 1000 cm⁻¹, FIG. 8B shows an expansion of the region from1750 to 900 cm⁻¹, and FIG. 8C shows a further expansion of the regionfrom 1250 to 950 cm⁻¹ which is associated with nucleic acids. Again,naked eye cannot found differences between the spectra of the control(50) and the pathological samples of hepatitis C in whole blood (51) andserum (52).

FIGS. 9A, 9B and 9C are plots depicting the result of a multivariateanalysis showing samples/scores of HIV in WB (□), hepatitis C virus inWB(▴), hepatitis B virus in serum (▾), Hepatitis C virus in serum (*)and HIV in serum (∘) against a control (⋄). Multivariate analysis isused to clarify differences or find hidden patterns. The method selectedis principal components analysis (PCA), an unsupervised method (i.e., itdoes not take into account the classes, only uses the spectra) thatprojects the samples in new coordinates orientated in the directions ofthe variance. In the scores of the PCA performed over the whole set ofdata, the two first scores space show separation among the serum (56)and whole blood (57) and in particular, among HIV (58) and hepatitis(60) virus samples.

FIG. 10 is a multivariate analysis of the serum spectra in two differentformats (FIG. 10A and FIG. 10B) for Hepatitis B (⋄), Hepatitis C (□) andHIV (▴). In the case of serum data, HIV-HIV and hepatitis are clearlyseparated in the PC1 (62) vs PC2 space (63).

FIG. 11 is a multivariate analysis of the serum spectra (hepatitis only)in two different formats (FIG. 11A and FIG. 11B). Although the visualinspection of the spectra did not reveal differences, in the comparisonbetween the Hepatitis B (⋄) and Hepatitis C (□) on the scores plot,there are different clusters for each illness.

FIG. 12 is a multivariate analysis of the serum spectra (hepatitis Bonly)(⋄). Labels indicate the log of the virus load. The sample with thehighest load is clearly separated from the others.

FIG. 13 is a multivariate analysis of the whole blood spectra in twodifferent formats (FIG. 13A and FIG. 13B). In the case of whole blood,the scores PC2 (70) and PC3 (71) clearly separate among the HIV (□),hepatitis C (▴) and the control (⋄).

FIG. 14 is an ATR-FTIR plot that allows comparison of spectra frominfected WB (73, 74) and control (75) using dry WB samples. FIG. 14Ashows the full spectrum from 3000 to 1000 cm⁻¹, FIG. 14B shows anexpansion of the region from 1750 to 900 cm⁻¹ and FIG. 14C shows afurther expansion of the region from 1250 to 950 cm⁻¹ which isassociated with nucleic acids.

FIG. 15 is an ATR-FTIR plot that allows comparison of spectra frominfected WB (77) and controls (78) using wet lysed blood samples. FIG.15A shows the full spectrum from 3000 to 1000 cm⁻¹, FIG. 15B shows anexpansion of the region from 1750 to 900 cm⁻¹.

FIG. 16 is an ATR-FTIR plot that allows comparison of spectra frominfected WB (80) and controls (81) using dry lysed blood samples. FIG.16A shows the full spectrum from 3000 to 1000 cm⁻¹, FIG. 16B shows anexpansion of the region from 1750 to 900 cm⁻¹.

FIG. 17 includes ATR-FTIR plots of methanol extracts of WB spiked withdifferent loadings of malaria (control (90); 0.0077% (91): 0.031% (92);0.25% (93); 0.49% (94); 1.96% (95). FIG. 17 shows an expansion of theregion from 1700 to 1728 cm⁻¹. A shift of the carbonyl band can beclearly seen, reflecting the spiking level.

FIG. 18 is a partial least squares regression plot of predicted vsactual level of parasite in the WB samples. The regression analysisenables discrimination between high and low parasite loadings. Note thatthe method is linear, and covers several orders of magnitude. Apermutation test indicated that the regression is significant at the 95%confidence level.

FIG. 19 is an ATR-FTIR spectrum of whole blood samples having a highviral load of HIV after subtraction of a control spectra.

FIG. 20 is a flow chart illustrating the steps according to oneembodiment of the method of the present invention.

FIG. 21 is a flow chart illustrating in more detail the steps of FIG.20, including an indication of the preferred data manipulation.

FIG. 22 is a flow chart illustrating the method of the present inventionwhen used to create a model for classifying blood samples as positive ornegative for hepatitis virus (Hep) or HIV.

FIG. 23 is a further flow chart depicting the creation of a suitablemode for classifying blood samples according to the present invention.

FIG. 24 is a plot of Y CV predicted against a linear index for methanolfixed RBC blood samples infected with malaria (□) and control (⋄)samples with Discrim Y 1 marked in broken line.

FIG. 25 is an IR absorption plot for the spectral window from 3000-2820cm⁻¹.

FIG. 26 is a plot of Y CV predicted against a linear index for methanolfixed RBC blood samples infected with malaria (□) and control (⋄)samples.

FIG. 27 is an IR absorption plot for the spectral window from 3000-2820cm⁻¹ and 1400-900 cm⁻¹.

FIG. 28 is an IR absorption plot for the spectral window from 4000-0cm⁻¹ for controls (100), and blood samples loaded with malariaparaseitemia (rings and trophocytes—RP (101), RNP (102) and TP (103)).

FIGS. 29A and 29B are plots in two different formats depicting theresult of a multivariate analysis of the results depicted in FIG. 28,showing samples/scores of multiple SPC files for the samples in theregion from 3116.19 to 2768.71 and 1828.46 to 852.91 cm⁻¹. (Control (⋄),RP (□), RNP (▴), TP (▾) with the broken oval line indicating 95%confidence level across x and y axes, also marked in broken lines. PC1(70.17%)(110) and PC2 (15.20%)(111) are included in FIG. 29B withdepicts variables/loading for multiple SPC files.

FIG. 30 is a partial least squares regression plot of predicted vsactual level of parasitemia in the samples depicted in FIG. 46. (Control(⋄), RP (□), RNP (▴), TP (▾)).

FIG. 31 illustrates visible images of cells that are stained (FIG. 31A)and IR images of untreated cells (FIG. 31B) based on mean spectra in theregion 2500 to 4000 cm⁻¹.

FIG. 31C is a visual close up of two cells (marked A and B, circled inFIG. 31A) with corresponding IR images (FIG. 31D).

FIG. 32 illustrates a PCA corresponding to FIG. 31 for the plasmodiuminfected area (⋄), the first uninfected are (▴) and second uninfectedarea (□).

FIG. 33 is a variables/loading plot.

FIG. 34 illustrates a supervised model PLSDA (without derivative) wasperformed showing the LV 1 component (unbroken line) and Reg Vector forY1 (broken line).

FIG. 35 is an IR spectrum for plasma samples bearing different types ofhepatitis (Hepatitis B (120); Hepatitis C (125)).

FIG. 36 is a partial least squares regression plot of predicted vsactual level of hepatitis in the samples (Hepatitis B (⋄); Hepatitis C(□)).

FIG. 37 is an FTIR spectrum for blood samples loaded with a range ofconcentrations of glucose and urea and dried on a glass fibre (100 mg/dl(130); 297 mg/dl (132); 490 mg/dl (134); 679 mg/dl (136); 865 mg/dl(138); neat glucose] (140)).

FIG. 38 is a partial least squares plot of the results for glucose asshown in FIG. 33. The unbroken line illustrates the best fit, and thebroken line illustrates the 1:1 correlation between predicted and spikedcorrelations.

FIG. 39 is a standard IR spectrum for glucose.

FIG. 40 is a partial least squares plot of the results for urea as shownin FIG. 33. The unbroken line illustrates the best fit, and the brokenline illustrates the 1:1 correlation between predicted and spikedcorrelations.

FIG. 41 is a standard IR spectrum of loading value against wavenumber.

FIG. 42 is an example of a typical graphical user interface for spectralquality control that would be displayed to a user.

DETAILED DESCRIPTION

The present invention will be further described with reference to thefollowing examples of protocols suitable for obtaining samples suitablefor ATR-IR analysis.

1. General Procedure for Crystal Cleaning

In general, the ATR crystal is cleaned using the following steps:

-   -   a) Humidified Soft cellulose is employed for eliminating the        sample.    -   b) The ATR Crystal is cleaned using soft cellulose and water        and/or organic solvents.    -   c) A spectrum of the empty crystal is obtained in order to        discard any memory effect.    -   d) If proteins are difficult to remove, it is recommended the        use of PBS, detergents or micellar water.        2. General Procedures for Sample Preparation        2.1 Whole blood (WB) Sampling Method

Whole blood is extracted from the patient in EDTA tubes or directly witha lancet.

2.1.1 Wet WB

A wet whole blood sample is typically processed according to the methodof the present invention to generate the ATR-FTIR spectrum similar tothose shown in FIG. 1 using the following steps:

-   -   a) A background is obtained using the empty clean ATR crystal.    -   b) A blank of water (W) is obtained by measuring the spectrum of        10 microliters of water.    -   c) 10 microliters of the EB are taken with a micropipette and        deposited on the surface of the ATR crystal.    -   d) Raw spectrum of whole blood (RWB) is immediately acquired.    -   e) The final spectrum (WB_(w)) of the whole blood is obtained by        subtracting the intensity of the i wavenumber of W from the i        wavenumber of RWB.        WB _(w)(i)=RWB(i)−W(i)    -   f) Crystal is cleaned according to the General Procedure.        2.1.2 Dry WB

A dry whole blood sample is typically processed according to the methodof the present invention to generate the ATR-FTIR spectrum shown in FIG.1 using the following steps:

-   -   a) A background is obtained using the empty clean ATR crystal.    -   b) Between 1 and 5 microlitres (the amount depending on the        application) are taken with a micropipette and deposited on the        surface of the ATR crystal.    -   c) Sample is dried through different methods (Allowing to        dry/using a drier or heat lamp)    -   d) After drying, spectrum (WB_(d)) of the whole blood is        acquired.    -   e) Crystal is cleaned according to the General Procedure set out        above.        2.2 Lysed WB Sample Method

WB samples are obtained in the same procedure as described above and arelysed by mixing whole blood with distillated water in a ratio 1:1 (v/v)or with a 7% (w/v) sodium dodecyl sulfate (SDS) solution at a ratio 8:1(v/v).

2.2.1 Wet Lysed WB

A wet lysed whole blood sample is typically processed according to themethod of the present invention to generate the ATR-FTIR spectrum shownin FIG. 1 using the following steps:

-   -   a) A background is obtained using the empty clean ATR crystal.    -   b) A bland of water (W) is obtained by measuring the spectrum of        10 microliters of distilled water or a blank of 7% (w/v) SDS        solution, mixed with distilled water at a ratio 8:1 (v/v),        depending on the method used on the lysis.    -   c) 10 microliters of lysed WB are taken with a micropipette and        deposited on the surface of the ATR crystal. Raw spectrum of        plasma (RL) is immediately obtained.    -   d) The final spectrum (L_(w)) of the lysed whole blood is        obtained subtracting the intensity of the i wavenumber of W to        the i wavenumber of RL.        L _(w)(i)=RL(i)−W(i)    -   e) Crystal is cleaned according to the General Procedure set out        above.        2.2.2 Dry Lysed WB

A dry lysed whole blood sample is typically processed according to themethod of the present invention to generate the ATR-FTIR spectrum shownin FIG. 1 using the following steps:

-   -   a) A background is obtained using the empty clean ATR crystal.    -   b) 1-5 (Depending on the application) microliters of lysed WB        are taken with a micropipette and deposited on the surface of        the ATR crystal.    -   c) Sample is dried through different methods (Allowing to        dry/using a drier or heat lamp)    -   d) After drying, spectrum (L_(d)) of the plasma is acquired.    -   e) Crystal is cleaned according to the General Procedure set out        above.        2.3 Plasma (P) Sample Method.

Patient plasma samples are typically prepared by first extracting wholeblood from the patient in ethylene diamine tetra acetic acid (EDTA)containing tubes or (or serum tubes if serum is required) directly witha lancet. WB samples are centrifuged at 1600 g during 10 minutes. Plasmais obtained from the upper phase with a Pasteur pipette.

2.3.1 Wet Plasma

A wet plasma sample is typically processed according to the method ofthe present invention to generate the ATR-FTIR spectrum shown in FIG. 1using the following steps:

-   -   a) A background is obtained from the empty clean ATR crystal.    -   b) A blank of water (W) is obtained by measuring the spectrum of        10 microliters of water.    -   c) 10 microliters of plasma are taken with a micropipette and        deposited on the surface of the ATR crystal. Raw spectrum of        plasma (RP) is immediately obtained.    -   d) The final spectrum (P_(w)) of the plasma is obtained        subtracting the intensity of the i wavenumber of W to the i        wavenumber of RP.        P _(w)(i)=RP(i)−W(i)    -   e) Crystal is cleaned according to the steps set out above.        2.3.2 Dry Plasma

A dry plasma sample is typically processed according to the method ofthe present invention to generate the ATR-FTIR spectrum shown in FIG. 1using the following steps:

-   -   a) A background is obtained using the empty clean ATR crystal.    -   b) 1 to 5 microlitres (the amount of depending on the        application) of plasma are taken with a micropipette and        deposited on the surface of the ATR crystal.    -   c) Sample is dried through different methods (Allowing to        dry/using a drier or heat lamp)    -   d) After drying, spectrum (P_(d)) of the plasma is acquired.    -   e) Crystal is cleaned according to the General Procedure set out        above.        2.4 Red Blood Cells (RBCs) Sample Method

A sample of patient RBCs are obtained by extracting whole blood from thepatient in EDTA tubes (or serum tubes if serum is required) or directlywith a lancet. WB samples are centrifuged at 1600 g during 10 minutes.RBCs are obtained from the lower phase with a Pasteur pipette.

2.4.1 Wet RBC

A wet RBC sample is typically processed according to the method of thepresent invention to generate the ATR-FTIR spectrum shown in FIG. 1using the following steps:

-   -   a) A background is obtained using the empty clean ATR crystal.    -   b) A blank of water (W) is obtained by measuring the spectrum of        10 microliters of water.    -   c) 10 microliters of RBCs are taken with a micropipette and        deposited on the surface of the ATR crystal. Raw spectrum of        RBCs (RRBCs) is immediately obtained.    -   d) The final spectrum (RBCs_(w)) of the plasma is obtained        subtracting the intensity of the i wavenumber of W to the i        wavenumber of RP.        RBCs _(w)(i)=RRBCs(i)−W(i)    -   e) Crystal is cleaned according to the General Procedure set out        above.        2.4.2 Dry RBC

A dry RBC sample is typically processed according to the method of thepresent invention to generate the ATR-FTIR spectrum shown in FIG. 1using the following steps:

-   -   a) A background is obtained using the empty clean ATR crystal.    -   b) 1-5 (Depending on the application) microliters of RBCs are        taken with a micropipette and deposited on the surface of the        ATR crystal.    -   c) Sample is dried through different methods (Allowing to        dry/using a drier or heat lamp)    -   d) After drying, spectrum (RBCs_(d)) of the plasma is obtained.    -   e) Crystal is cleaned according to the General Procedure set out        above.        2.5 RBC Packed in Solvent

An RBC sample in solvent, such as methonal (MeOH) is typically processedaccording to the method of the present invention to generate theATR-FTIR spectrum shown in FIG. 1 using the following steps:

-   -   a) RBC obtained are washed with PBS (phosphate buffered saline),        to remove the plasma/serum components and then mixed with 1 mL        of cold methanol 0.4:1 (v/v).    -   b) 1-5 (Depending on the application) microliters of the RBCs        packed in methanol are taken with a micropipette and deposited        on the surface of the ATR crystal.    -   c) Sample is dried through different methods (Allowing to        dry/using a drier or heat lamp)    -   d) After drying, spectrum (RBCsm) of the RBC packed in methanol        is obtained.    -   e) Crystal is cleaned according to the General Procedure set out        above.        2.5 Slurry of Coagulated Whole Blood in a Solvent

A slurry of coagulated whole blood in a solvent, such as methanol (MeOH)is typically processed according to the method of the present inventionto generate the ATR-FTIR spectrum shown in FIG. 1 using the followingsteps:

-   -   a) WB is extracted using the same procedure as in section 1.1.    -   b) 1-5 microliters of WB are deposited on the ATR crystal with a        micropipette.    -   c) The same amount of methanol is deposited on the previous drop        of WB, creating a slurry of coagulate blood.    -   d) Sample is dried through different methods (Allowing to        dry/using a drier or heat lamp)    -   e) After drying, spectrum of the dry slurry is acquired.    -   f) Cleaning of the crystal according to the General Procedure        set out above        2.6 Serum/Plasma/Blood Lipid Extracts

Lipid extracts from serum, or plasma or blood or a combination thereofin a solvent, such as methanol (MeOH) is typically processed accordingto the method of the present invention to generate the ATR-FTIR spectrumshown in FIG. 2 using the following steps:

-   -   a) WB/Plasma/Serum is extracted using the same procedure as in        section 1.1.    -   b) WB/Plasma/Serum is mixed with an organic solvent. If emulsion        is formed, sample should be centrifuged.    -   c) 1-5 microliters of the extracting phase are taken with a        micropipette and deposited on the ATR crystal.    -   d) Sample is dried through different methods (Allowing to        dry/using a drier or heat lamp)    -   e) After drying, spectrum of the dry film is acquired.    -   f) Cleaning of the crystal according to the General Procedure        above.        Malaria as the Disease Agent

Malaria is caused by different species of Plasmodium. The differentspecies of plasmodium have a different molecular phenotype andcorresponding infrared spectra. Different species of Malaria causativeagent are included in the Malaria reference database. To speciate oridentify the different species of plasmodium typically one would use thefollowing method first to identify that the person has malaria such asthe following.

Accordingly, in a further embodiment of the method of detecting malariain a blood sample according to the present invention, the methodcomprises the steps of:

-   -   (i) creating a sample infra-red spectrum representative of the        blood sample, the sample spectrum having one or more spectral        components, each component having a wavenumber and absorbance        value.    -   (ii) providing a reference database of spectral models, each        model having one or more database spectral components of a        wavenumber and an absorbance value, wherein the database        spectral components identify malaria,    -   (iii) determining whether the reference database has one or more        database spectral components corresponding to one or more sample        spectral components, and    -   (iv) compiling a list of corresponding database components        identified.

In a further embodiment of the method of the present invention, tospeciate and determine the causative agent of malaria into the variousPlasmodium species, the method comprises the steps of:

-   -   (i) creating a sample infra-red spectrum representative of the        blood sample, the sample spectrum having one or more spectral        components, each component having a wavenumber and absorbance        value.    -   (ii) providing a reference database of spectral models, each        model having one or more database spectral components of a        wavenumber and an absorbance value, wherein the database        spectral components identify the different Plasmodium species        such (as and not limited to) Plasmodium falciparum, Plasmodium        vivax, Plasmodium ovale curtisi, Plasmodium ovale wallikeri,        Plasmodium malariae, Plasmodium knowlesi or combinations        thereof,    -   (iii) determining whether the reference database has one or more        database spectral components corresponding to one or more sample        spectral components, and    -   (iv) compiling a list of corresponding database components        identified.

Experimental Results—Malaria

Experimental test carried out using the above methods have shown acorrelation between the spectra and malaria parasite concentration inblood. Red blood cells (RBC) and whole plasma samples (WB) loaded withdifferent concentrations of parasitemia (rings and trophocytes) weredried in glass fibre paper. The loading regime is summarised in Table 4:

TABLE 4 Parasitemia (level of Type of Blood Sample Type of Parasitemialoading) RBC CONTROL 0 RBC CONTROL 0 RBC CONTROL 0 RBC CONTROL 0 RBCCONTROL 0 WB CONTROL 0 WB CONTROL 0 WB CONTROL 0 WB CONTROL 0 WB CONTROL0 GF TR GF TR 0 GF UNT GF UNT 0 RBC RING 0.078125 RBC RING 0.15625 RBCRING 0.3125 RBC RING 0.625 RBC RING 1.25 RBC RING 10 RBC RING 10 RBCRING 2.5 RBC RING 5 RBC RING 5 WB RING 0.078125 WB RING 0.15625 WB RING0.3125 WB RING 10 WB RING 2.5 WB RING 5 RBC TROPHOCYTE 0.078125 RBCTROPHOCYTE 0.15625 RBC TROPHOCYTE 0.3125 RBC TROPHOCYTE 0.625 RBCTROPHOCYTE 1.25 RBC TROPHOCYTE 2.5 RBC TROPHOCYTE 5 WB TROPHOCYTE0.078125 WB TROPHOCYTE 0.15625 WB TROPHOCYTE 1.25 WB TROPHOCYTE 5

FIG. 28 is aa IR absorption spectrum of the control, RP, RNP and TPsamples loaded with malaria as listed in Table 4. The naked eye cannotreadily distinguish differences between the spectra of the control andthe pathological samples.

FIGS. 29A and 29B are plots in two different formats depicting theresult of a multivariate analysis showing samples/scores of multiple SPCfiles for the samples in the region from 3116.19 to 2768.71 and 1828.46to 852.91 cm⁻¹. Multivariate analysis is used to clarify differences orfind hidden patterns. The method selected is principal componentsanalysis (PCA), an unsupervised method (i.e., it does not take intoaccount the classes, only uses the spectra) that projects the samples innew coordinates orientated in the directions of the variance.

FIG. 30 is a partial least squares regression plot of predicted vsactual level of parasitemia in the samples. The regression analysisenables discrimination between high and low parasite loadings. Note thatthe method is linear, and covers several orders of magnitude.

Further experimental testing was carried out to see whether the IRsignature of the malarial trophocyte on the RBC was maintained whendried in the paper. Ten RBC samples were loaded with 5% paraseitemia(trophocytes) and dried on normal filter paper. Twelve normal RBCsamples were also created as controls.

The method of the present invention has also been used for detection inrespect of samples known to contain Plasmodium falciparum and/orPlasmodium vivax by microscopy and PGR. The results using FTRdemonstrated that the method was suitable for detection of infection byboth malarial species and mixed infection.

Experimental Results—Detection of Malaria Using Images Obtained fromThin Smears of RBC in Glass

Experimental investigations were undertaken to investigate the efficacyof the method of the present invention with respect to distinguishingbetween RBCs infected with 5% malarial trophozoites, an uninfected bloodcells.

In this case Focal Plane Array was used, that is, FP spectroscopicimaging of thin blood smears on glass. After image acquisition thesamples were stained with Giemsa stain for the visual detection of thetrophozoites.

FIG. 31 shows visible images of cells that are stained (FIG. 31A) and IRimages of untreated cells (FIG. 31B) based on mean spectra in the region2500 to 4000 cm⁻¹. FIG. 31C is a visual close up of two cells (marked Aand B, circled in FIG. 31a ) with corresponding IR images (FIG. 31D). Itis clear from these images that the density of the spectra is greaterwhen the parasite is not present. PCA analysis reveals threeareas—uninfected RBC areas (112), plasmodium for the trophozoites (113)and a second uninfected area for the part of the infected RBC withoutthe trophozoite (114).

The corresponding PCA is recorded in FIG. 32 for the plasmodium infectedarea (⋄), the first uninfected are (▴) and second uninfected area (□).The 95% confidence level is marked in at −4 and 4.

FIG. 33 is a variables/loadings plot. In order to more closely examinethe differences, a supervised model PLSDA (without derivative) wasperformed and is illustrated at FIG. 34. The LV 1 component (unbrokenline) and Reg Vector for Y1 (broken line) are shown. Although theregression vector is quite noisy, there is a shift at the 3300 cm⁻¹ bandwhich is able to discriminate between infected and non-infected pixels.

Based on the aforementioned results the following methodology for theidentification of paraseitemia in untreated RBC thin films on glass canbe proposed:

-   -   (i) create a thin blood film,    -   (ii) carry out microscopic visual analysis and create a visual        image,    -   (iii) create an FTIR image,    -   (iv)(a) model each pixel of the image in order to classify them        as parasite or RBC,    -   (iv)(b) extract RBC means spectra, averaging the pixels of each        RBC and investigate whether each RBC is infected or not.

Experimental Results—Hepatitis

Experimental tests carried out using the above methods have shows thatit is possible to distinguish between plasma samples bearing differenttypes of hepatitis.

For each sample, approximately 3 microliters of plasma bearing HepatitisB (HB) and Hepatitis C (HC) was placed onto pre-cut glass filter paperand air-dried for 20 minutes. The glass paper with the dried plasmasample was then placed onto the crystal of a diamond ATR-FTIR window anda spectrum recorded at 8 cm⁻¹ with 50 scans co-added and ratioed againsta background spectrum of air. The resulting spectrum is illustrated inFIG. 35.

FIG. 36 is a partial least squares regression plot of predicted vsactual level of hepatitis in the samples. The data analysis was carriedout for the samples in the region from 1583.18 to 1492.13 cm⁻¹ and1304.45 to 1120.49 cm⁻¹. The regression analysis enables discriminationbetween high and low parasite loadings and between HB and HC infection.

Experimental Results—Glucose & Urea

The previous experimental results illustrated spectral effects relatingto IR energy absorbed directly by a disease agent in the form ofparasitemia present in the blood. Experimental tests carried out usingthe method of the present invention have also know that it is possibleto detect a disease agent indirectly, via the energy absorbed by otherbiological entities caused by the disease agent. For example, thedisease agent may cause rises in glucose, urea or both.

Blood samples were loaded with a wide range of concentrations of glucoseand urea and dried on glass fibre. FIG. 37 illustrates the FTIR spectrumfor the samples and illustrates the absorbance of glucose isproportional to the concentration of glucose in the sample.

FIG. 38 provides a partial least squares plot of the results. Aregression vector was then correlated with the glucose standard spectrumas shown in FIG. 39.

A similar approach was taken with urea. FIG. 40 provides a partial leastsquares plot of the results. A regression vector was then correlatedwith the glucose standard spectrum as shown in FIG. 41.

Experimental Results—Quality Controls

Validation of the spectra can be carried out prior to inclusion into useof the aforementioned models. This ensures that an acquired spectrum hasfeatures similar to the features included in the model. It also ensuresthat technical issues are not going to interfere in the extraction ofinformation from the model. For example, the following two methods ofquality control were developed.

Quality Control—Model Independent

The first relies on quality control independent of the model that is,depending only on the database. The quality control focuses on trying tomonitor excesses (or defects) of the different of components andinterferences pertaining to the sample. The component relativeconcentration is calculated using an algorithm, and this concentrationis compared with a threshold value. For example, a distribution ofrelative concentration values of the component can be created on thedatabase. Then the portions of the distribution that tail off at theupper and lower ends can be used for defining the threshold. If therelative concentration of the component is outside the threshold, thespectrum does not pass the quality control.

Typically, the following three components are considered sequentially inthis quality control method:

-   -   (i) Atmospheric interferences: Fluctuation of IR active        atmospheric vapours between the background and sample        measurements can cause negative and positive bands which are        detected by using a positive and negative thresholds;    -   (ii) Solvent: The solvent (Water, MeOH) has not been properly        eliminated; and    -   (iii) Sample: There is not enough sample on the crystal, for        example, due to bad contact.

Quality Control—Model Dependent

The second quality control method is associated with the model andrelies on measurement of the distance between the sample and thecalibration samples in terms of the modelling. A typical example is theuse of the T² and SQ residuals on a PLSDA and a 95% confidence interval.

For example, the quality control for a spectrum recorded could becarried out in the sequence (i) atmospheric interference (water), (ii)solvent (methanol), (iii) sample, and finally (iv) distance to themodel. Typically this would correlate with results such as those inTable 5:

TABLE 5 Calculation of relative QC Pre-processing concentrationThresholds H₂O(g) Normalization Abs at 3846 cm⁻¹ − <1.5 SD >1.5 Abs at3852 cm⁻¹ SD MeOH Derivative Abs at 1029 cm⁻¹ − >1.5 SD Abs at 1033 cm⁻¹Sample none Absorbance at 1650 cm⁻¹ <1.5 SD

An example of a typical graphical user interface that would be displayedto the user is depicted in FIG. 42.

While this invention has been described in connection with specificembodiments thereof, it will be understood that it is capable of furthermodification(s). This application is intended to cover any variationsuses or adaptations of the invention following in general, theprinciples of the invention and including such departures from thepresent disclosure as come within know or customary practice within theart to which the invention pertains and as may be applied to theessential features hereinbefore set forth.

As the present invention may be embodied in several forms withoutdeparting from the spirit of the essential characteristics of theinvention, it should be understood that the above described embodimentsare not to limit the present invention unless otherwise specified, butrather should be construed broadly within the spirit and scope of theinvention as defined in the appended claims. The described embodimentsare to be considered in all respects as illustrative only and notrestrictive.

Various modifications and equivalent arrangements are intended to beincluded within the spirit and scope of the invention and appendedclaims. Therefore, the specific embodiments are to be understood to beillustrative of the many ways in which the principles of the presentinvention may be practiced. In the following claims, means-plus-functionclauses are intended to cover structures as performing the definedfunction and not only structural equivalents, but also equivalentstructures.

It should be noted that where the terms “server”, “secure server” orsimilar terms are used herein, a communication device is described thatmay be used in a communication system, unless the context otherwiserequires, and should not be construed to limit the present invention toany particular communication device type. Thus, a communication devicemay include, without limitation, a bridge, router, bridge-router(router), switch, node, or other communication device, which may or maynot be secure.

It should also be noted that where a flowchart is used herein todemonstrate various aspects of the invention, it should not be construedto limit the present invention to any particular logic flow or logicimplementation. The described logic may be partitioned into differentlogic blocks (e.g., programs, modules, functions, or subroutines)without changing the overall results or otherwise departing from thetrue scope of the invention. Often, logic elements may be added,modified, omitted, performed in a different order, or implemented usingdifferent logic constructs (e.g., logic gates, looping primitives,conditional logic, and other logic constructs) without changing theoverall results or otherwise departing from the true scope of theinvention.

Various embodiments of the invention may be embodied in many differentforms, including computer program logic for use with a processor (e.g.,a microprocessor, microcontroller, digital signal processor, or generalpurpose computer and for that matter, any commercial processor may beused to implement the embodiments of the invention either as a singleprocessor, serial or parallel set of processors in the system and, assuch, examples of commercial processors include, but are not limited toMerced™, Pentium™, Pentium II™, Xeon™, Celeron™, Pentium Pro™,Efficeon™, Athlon™, AMD™ and the like), programmable logic for use witha programmable logic device (e.g., a Field Programmable Gate Array(FPGA) or other PLD), discrete components, integrated circuitry (e.g.,an Application Specific Integrated Circuit (ASIC)), or any other meansincluding any combination thereof. In an exemplary embodiment of thepresent invention, predominantly all of the communication between usersand the server is implemented as a set of computer program instructionsthat is converted into a computer executable form, stored as such in acomputer readable medium, and executed by a microprocessor under thecontrol of an operating system.

Computer program logic implementing all or part of the functionalitywhere described herein may be embodied in various forms, including asource code form, a computer executable form, and various intermediateforms (e.g., forms generated by an assembler, compiler, linker, orlocator). Source code may include a series of computer programinstructions implemented in any of various programming languages (e.g.,an object code, an assembly language, or a high-level language such asFortran, c, C++, JAVA, or HTML. Moreover, there are hundreds ofavailable computer languages that may be used to implement embodimentsof the invention, among the more common being Ada; Algol; APL; awk;Basic; C; C++; Conol; Delphi; Eiffel; Euphoria; Forth; Fortran; HTML;Icon; Java; Javascript; Lisp; Logo; Mathematica; MatLab; Miranda;Modula-2; Oberon; Pascal; Perl; PL/I; Prolog; Python; Rexx; SAS; Scheme;sed; Simula; Smalltalk; Snobol; SQL; Visual Basic; Visual C++; Linux andXML) for use with various operating systems or operating environments.The source code may defined and use various data structures andcommunication messages. The source code may be in a computer executableform (e.g., via an interpreter), or the source code may be converted(e.g., via a translator, assembler, or compiler) into a computerexecutable form.

The computer program may be fixed in any form (e.g., source code form,computer executable form, or an intermediate form) either permanently ortransitorily in a tangible storage medium, such as a semiconductormemory device (e.g., a RAM, ROM, PROM, EEPROM, or Flash-ProgrammableRAM), a magnetic memory device (e.g., a diskette or fixed disk), anoptical memory device (e.g., a CD-ROM or DVD-ROM), a PC card (e.g.,PCMCIA card), or other memory device. The computer program may be fixedin any form in a signal that is transmittable to a computer using any ofvarious communication technologies, including, but in no way limited to,analog technologies, digital technologies, optical technologies,wireless technologies (e.g., Bluetooth), networking technologies, andinter-networking technologies. The computer program may be distributedin any form as a removable storage medium with accompanying printed orelectronic documentation (e.g., shrink wrapped software), preloaded witha computer system (e.g., on system ROM or fixed disk), or distributedfrom a server or electronic bulletin board over the communication system(e.g., the Internet or World Wide Web).

Hardware logic (including programmable logic for use with a programmablelogic device) implementing all or part of the functionality wheredescribed herein may be designed using traditional manual methods, ormay be designed, captured, simulated, or documented electronically usingvarious tools, such as Computer Aided Design (CAD), a hardwaredescription language (e.g., VHDL or AHDL), or a PLD programming language(e.g., PALASM, ABEL, or CUPL). Hardware logic may also be incorporatedinto display screens for implementing embodiments of the invention andwhich may be segmented display screens, analogue display screens,digital display screens, CRTs, LED screens, Plasma screens, liquidcrystal diode screen, and the like.

Programmable logic may be fixed either permanently or transitorily in atangible storage medium, such as a semiconductor memory device (e.g., aRAM, ROM, PROM, EEPROM, or Flash-Programmable RAM), a magnetic memorydevice (e.g., a diskette or fixed disk), an optical memory device (e.g.,a CD-ROM or DVD-ROM), or other memory device. The programmable logic maybe fixed in a signal that is transmittable to a computer using any ofvarious communication technologies, including, but in no way limited to,analog technologies, digital technologies, optical technologies,wireless technologies (e.g., Bluetooth), networking technologies, andinternetworking technologies. The programmable logic may be distributedas a removable storage medium with accompanying printed or electronicdocumentation (e.g., shrink wrapped software), preloaded with a computersystem (e.g., on system ROM or fixed disk), or distributed from a serveror electronic bulletin board over the communication system (e.g., theInternet or World Wide Web).

“Comprises/comprising” and “includes/including” when used in thisspecification is taken to specify the presence of stated features,integers, steps or components but does not preclude the presence oraddition of one or more other features, integers, steps, components orgroups thereof. Thus, unless the context clearly requires otherwise,throughout the description and the claims, the words ‘comprise’,‘comprising’, ‘includes’, ‘including’ and the like are to be construedin an inclusive sense as opposed to an exclusive or exhaustive sense;that is to say, in the sense of “including, but not limited to”.

The invention claimed is:
 1. A method of detecting hepatitis,comprising: obtaining a background spectrum from an empty clean ATRcrystal; drying a plasma sample; acquiring a spectrum of the driedplasma using the ATR crystal; and performing a partial least squaresdiscriminate analysis on the spectrum using wavenumber ranges from1583.18 to 1492.13 cm⁻¹ and 1304.45 to 1120.49 cm⁻¹ to discriminatebetween Hepatitis B and Hepatitis C infection.
 2. The method of claim 1,further comprising: placing the plasma sample on glass filter paper; andwherein drying the plasma sample comprises drying the plasma sample onthe glass filter paper.
 3. The method of claim 2, wherein drying theplasma sample comprises air-drying the sample for 20 min.
 4. The methodof claim 2, further comprising transferring the dried plasma sample fromthe glass filter paper to the ATR crystal.
 5. The method of claim 1,further comprising: pipetting 1 to 5 microliters of plasma on a surfaceof the ATR crystal; and drying the plasma on the surface of the ATRcrystal.
 6. The method of claim 1, further comprising checking awavenumber range from 3750 to 3950 cm⁻¹ to confirm sufficient drying ofthe plasma sample.
 7. The method of claim 1, further comprising checkinga wavenumber range from 1600 to 1700 cm⁻¹ to confirm sufficient contactof the dried plasma sample.
 8. The method of claim 1, whereinidentifying the presence of hepatitis C comprises reporting aprobability of the presence of hepatitis C.
 9. The method of claim 1,further comprising identifying the presence of hepatitis B based on a cvvalue of 0.5 or less.
 10. The method of claim 9, wherein identifying thepresence of hepatitis B comprises reporting a probability of thepresence of hepatitis B.